StyleBin: Stylizing Video by Example in Stereo

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00361484" target="_blank" >RIV/68407700:21230/22:00361484 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1145/3550469.3555420" target="_blank" >https://doi.org/10.1145/3550469.3555420</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3550469.3555420" target="_blank" >10.1145/3550469.3555420</a>

Jazyk výsledku

angličtina

Název v původním jazyce

StyleBin: Stylizing Video by Example in Stereo

Popis výsledku v původním jazyce

In this paper we present StyleBin—an approach to example-based stylization of videos that can produce consistent binocular depiction of stylized content on stereoscopic displays. Given the target sequence and a set of stylized keyframes accompanied by information about depth in the scene, we formulate an optimization problem that converts the target video into a pair of stylized sequences, in which each frame consists of a set of seamlessly stitched patches taken from the original stylized keyframe. The aim of the optimization process is to align the individual patches so that they respect the semantics of the given target scene, while at the same time also following the prescribed local disparity in the corresponding viewpoints and being consistent in time. In contrast to previous depth-aware style transfer techniques, our approach is the first that can deliver semantically meaningful stylization and preserve essential visual characteristics of the given artistic media. We demonstrate the practical utility of the proposed method in various stylization use cases.

Název v anglickém jazyce

StyleBin: Stylizing Video by Example in Stereo

Popis výsledku anglicky

In this paper we present StyleBin—an approach to example-based stylization of videos that can produce consistent binocular depiction of stylized content on stereoscopic displays. Given the target sequence and a set of stylized keyframes accompanied by information about depth in the scene, we formulate an optimization problem that converts the target video into a pair of stylized sequences, in which each frame consists of a set of seamlessly stitched patches taken from the original stylized keyframe. The aim of the optimization process is to align the individual patches so that they respect the semantics of the given target scene, while at the same time also following the prescribed local disparity in the corresponding viewpoints and being consistent in time. In contrast to previous depth-aware style transfer techniques, our approach is the first that can deliver semantically meaningful stylization and preserve essential visual characteristics of the given artistic media. We demonstrate the practical utility of the proposed method in various stylization use cases.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Výzkumné centrum informatiky</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

SIGGRAPH Asia 2022 Conference Papers

ISBN

9781450394703

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

—

Název nakladatele

ACM SIGGRAPH

Místo vydání

New York

Místo konání akce

Daegu

Datum konání akce

6. 12. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—